The impossibility theorem of fairness is a foundational result in the algorithmic fairness literature. It states that outside of special cases, one cannot exactly and simultaneously satisfy all three common and intuitive definitions of fairness - demographic parity, equalized odds, and predictive rate parity. This result has driven most works to focus on solutions for one or two of the metrics. Rather than follow suit, in this paper we present a framework that pushes the limits of the impossibility theorem in order to satisfy all three metrics to the best extent possible. We develop an integer-programming based approach that can yield a certifiably optimal post-processing method for simultaneously satisfying multiple fairness criteria under small violations. We show experiments demonstrating that our post-processor can improve fairness across the different definitions simultaneously with minimal model performance reduction. We also discuss applications of our framework for model selection and fairness explainability, thereby attempting to answer the question: who's the fairest of them all?

The wide spread of false information online including misinformation and disinformation has become a major problem for our highly digitised and globalised society. A lot of research has been done to better understand different aspects of false information online such as behaviours of different actors and patterns of spreading, and also on better detection and prevention of such information using technical and socio-technical means. One major approach to detect and debunk false information online is to use human fact-checkers, who can be helped by automated tools. Despite a lot of research done, we noticed a significant gap on the lack of conceptual models describing the complicated ecosystems of false information and fact checking. In this paper, we report the first graphical models of such ecosystems, focusing on false information online in multiple contexts, including traditional media outlets and user-generated content. The proposed models cover a wide range of entity types and relationships, and can be a new useful tool for researchers and practitioners to study false information online and the effects of fact checking.

While most frameworks for online sequential decision-making focus on the objective of maximizing reward, in practice this is rarely the sole objective. Other considerations may involve budget constraints, ensuring fair treatment, or estimating various population characteristics. There has been growing recognition that these other objectives must be formally integrated into sequential decision-making frameworks, especially if such algorithms are to be used in sensitive application areas [21]. In this work, we focus on the problem of maximizing reward while simultaneously estimating the population total (equivalently, mean) in a structured bandit setting. The most natural approach to this problem from a machine learning perspective is to use a model to predict the mean. However, this method is subject to the problem that adaptively collected data are subject to bias, which in turn biases the model estimates [29].

Responsible AI, Ethical AI, AI for social good -- I am sure you must have heard these terms at some point or the other, whether you are a Data Scientist or not. "The development of full artificial intelligence could spell the end of the human race." And there my journey of understanding this critical aspect of the AI foundation started. I used to wonder how to relate ethics with AI which is just a series of algorithms, when, in fact, we have not been able to apply ethical behavior among ourselves. As per the AI index report published by the Stanford University Institute for Human-Centered AI, cybersecurity and regulatory compliance are among the top risks identified by AI/ML-oriented organizations. Another report reveals how AI has captured interest among undergraduate students.

After 38 years as the head of the National Institute of Allergy and Infectious Diseases, Dr. Anthony Fauci announced on Monday that he will be stepping down from his role in December. Appointed to the position in 1984 by then-president Ronald Reagan, Fauci has personally overseen the federal government's response to some of the 20th century's deadliest infectious diseases -- from tuberculosis and COVID to SARS and MERS. But, as he told The Guardian in 2020, "my career and my identity has really been defined by HIV." The prevention and treatment of HIV has been a prioritized area of research for the NIAID since 1986, and one that Dr. Fauci has devoted much of his public service to. The current state of AIDS research and response in America is thanks in no small part to his continued efforts in the field.

A tiny robot known as MIRA will be blasting off to the International Space Station (ISS) in 2024 to perform simulated surgical procedures in microgravity. MIRA, or "Miniaturized in vivo Robotic Assistant," will fly to the International Space Station thanks to a $100,000 award to the University of Nebraska-Lincoln through the U.S. Department of Energy's Established Program to Stimulate Competitive Research (EPSCoR). The technology involved could in the future provide a solution to medical emergencies requiring surgical intervention while astronauts are far from home, such as on a mission to Mars. First though, the 2024 test mission will see MIRA operate within an experimental locker the size of a microwave aboard ISS in low-Earth orbit. The aim will be to fine-tune the robot's operation in microgravity through autonomous tests including cutting stretched rubber bands and pushing metal rings along a wire, mimicking movements used in surgery.

Artificial Intelligence (AI) is revolutionizing cybersecurity. As cyber threats grow in volume and complexity, new systems and measures for dealing with them become more important than ever. AI can be used to evaluate vast amounts of data on potential risks, helping to improve security operations and speeding up response times. Since they can quickly analyze millions of events and identify a wide range of threats, including malware that exploits zero-day vulnerabilities and risky behaviour that could result in phishing attacks or the download of malicious code, AI and machine learning (ML) have emerged as crucial technologies in information security. These technologies develop over time and use historical data to recognize current emerging sorts of threats.

What may be the most appropriate definition of Artificial Intelligence (AI)? What are the broad requirements to develop and deploy AI models in a telecom sector? What are the challenges faced by telecom service providers in adopting AI? How can big data (BD) in the telecom sector be utilised for developing AI models? These are some of the questions that the Telecom Regulatory Authority of India (TRAI) is looking to deal with in its new consultation paper which seeks to understand how artificial intelligence and big data can be used in the telecom sector. The paper is open for consultation till September 16, 2022 and one can send their counter comments till September 30, 2022.

Artificial Intelligence (AI) is often used for multiple purposes such as automating repetitive tasks and logically inputting data. AI technology has the capability to replicate a human's mind where it can learn behaviour and interpret data. AI is used in many industries for tasks such as facial recognition and assisting with self-driving cars. As organisations become more complex, and structures are constantly evolving, staff can no longer use traditional methods to identify weaknesses and are turning to AI for cybersecurity. On the other hand, cybercriminals have increased opportunities to access a company's network infrastructure, as businesses become more complex, hackers have more exploits to use against them.

A former Apple engineer has pleaded guilty to trade secret theft -- one of two people accused of stealing trade secrets from the iPhone maker's nascent self-driving car program. United States federal prosecutors have alleged that Xiaolang Zhang downloaded the plan for a circuit board for Apple's self-driving system after disclosing his intentions to work for a Chinese self-driving car startup and booking a last-minute flight to China. He was arrested at the San Jose airport after he passed through a security checkpoint. Zhang initially pleaded not guilty to the charges, but according to court documents on Monday, he had reached a plea deal with prosecutors and changed his plea to guilty. The plea deal is sealed and sentencing is set for November.